Key policy and programmatic factors to improve influenza vaccination rates based on the experience from four high-performing countries
Bibliographic record
Abstract
BACKGROUND: Many countries consistently fail to achieve the target influenza vaccine coverage rate (VCR) of 75% for populations at risk of complications, recommended by the World Health Organization and European Council. We aimed to identify factors for achieving a high VCR in the scope of four benchmark countries with high influenza VCRs: Australia, Canada, UK and USA. METHODS: Publicly available evidence was first reviewed at a global level and then for each of the four countries. Semi-structured interviews were then conducted with stakeholders meeting predefined criteria. Descriptive cluster analyses were performed to identify key factors and pillars for establishing and maintaining high VCRs. RESULTS: No single factor led to a high VCR, and each benchmark country used a different combination of tailored approaches to achieve a high vaccine coverage. In each country, specific triggers were important to stimulate changes that led to improved vaccine coverage. A total of 42 key factors for a successful influenza vaccination programme were identified and clustered into five pillars: (1) Health Authority accountability and strengths of the influenza programme, (2) facilitated access to vaccination, (3) healthcare professional accountability and engagement, (4) awareness of the burden and severity of disease and (5) belief in influenza vaccination benefit. Each benchmark country has implemented multiple factors from each pillar. CONCLUSION: A wide range of factors were identified from an evaluation of four high-performing benchmark countries, classified into five pillars, thus providing a basis for countries with lower VCRs to tailor their own particular solutions to improve their influenza VCR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".